EEG-Based Classification of the Driver Alertness State

• Main Authors: Golz Martin, Thomas Sebastian, Schenka Adolf
• Format: Article
• Language: English
• Published: De Gruyter 2020-09-01
• Series: Current Directions in Biomedical Engineering
• Subjects: electroencephalogram; eeg; driving simulation; drowsiness; classification; machine learning; generalized matrix relevance learning vector quantization; support-vector machine; gradient boosting machine
• Online Access: https://doi.org/10.1515/cdbme-2020-3091

GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.